Ph.D. Position Opportunity at Mondragon University (Spain): Condition-Based Maintenance Planning for Wave Energy Technology through Prognostics & Health Management Strategies

Offshore renewable energy technologies have the potential to support traditional renewable energy sources and contribute to the energy mix in the short-medium term. Wave energy is an emerging area among the offshore renewable energy systems, where the motion of ocean waves is converted into electricity. Different technologies have been suggested [1], but all include different components to convert mechanical energy into electricity [2,3], which have a significant impact on the final performance and maintenance of the wave energy devices. The nature of the operating environment generates operation and maintenance challenges that hamper the wide implementation and adoption of the wave energy technology.

There are different assets involved in the wave energy conversion and distribution processes such as turbines, converters, generators, batteries, transformers, cables, or circuit breakers. In the context of traditional power grids, the operation and degradation processes of these components are well studied [4-6]. However, in the context of wave energy, open sea and weather conditions determine when it is possible to travel to the offshore power plant and perform maintenance actions. These travel time instants are known as weather windows. Accordingly, failures of system components can result in prolonged periods of downtime and this situation can significantly limit the benefits of wave energy applications. In this context, effective risk management and maintenance planning are crucial activities for the effective implementation and adoption offshore technologies.

The objective of this project is to infer maintenance planning strategies through monitoring the health state of the assets that take part in the wave-to-electricity energy conversion process along with sea and weather operation conditions. These strategies will have to evaluate the risk of failure of components, cost of maintenance actions and weather windows, and accordingly elicit optimal maintenance windows. This process will involve the development and application of prognostics, and health management techniques via engineering knowledge combined with artificial intelligence and reliability methods, e.g. [7, 8].

The models developed in this project will be validated with the data collected from real offshore facilities located in the Basque Country through the collaboration with the Biscay Marine Energy Platform (BIMEP) which owns two pioneering offshore facilities. The Mutriku Wave Power Plant in Mutriku and the testing site located in Armintza.

The project will be developed in Mondragon Unibertsitatea within the group of Signal Theory and communications (Joxe Aizpurua) in collaboration with the Fluid Mechanics group (Markel Peñalba). Throughout the thesis the student will engage continuously with industry and stays at different universities and/or research centers will be pursued.

Interested applicants please apply here:

If you have any further questions or comments please refer to: [email protected] and [email protected]

Application deadline. Review of applications will begin November 7th and continue until the position is filled.


• M. Sc. degree in telecommunications, electronics, computer science, embedded systems or a related area.
• Programming skills: Matlab, Python, R, or C++
• Knowledge/experience with renewable energies is a plus.
• Knowledge/experience with reliability and/or diagnostics/health management methods is a plus.
• Experience with artificial intelligence methods is a plus.

[1] Falcao, A. F. de O. (2010). Wave energy utilization: A review of the technologies. Renewable and Sustainable Energy Reviews, 14(3), 899–918.
[2] Penalba, M., & Ringwood, J. V. (2016). A review of wave-to-wire models for wave energy converters. Energies, 9(7), 506.
[3] Penalba, M., & Ringwood, J. V. (2019). A high-fidelity wave-to-wire model for wave energy converters. Renewable energy, 134, 367-378.
[4] Aizpurua, J. I., Stewart, B. G., McArthur, S. D. J., Lambert, B., Cross, J. G., & Catterson, V. M. (2019). Improved power transformer condition monitoring under uncertainty through soft computing and probabilistic health index. Applied Soft Computing, 105530.
[5] Aizpurua, J. I., Catterson, V. M., Abdulhadi, I. F., & Garcia, M. S. (2017). A model-based hybrid approach for circuit breaker prognostics encompassing dynamic reliability and uncertainty. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(9), 1637-1648.
[6] U. Garro, E. Muxika, J. I. Aizpurua and M. Mendicute (2019). FPGA-Based Stochastic Activity Networks for On-Line Reliability Monitoring. IEEE Transactions on Industrial Electronics.
[7] Aizpurua, J. I., Catterson, V. M., Papadopoulos, Y., Chiacchio, F., & Manno, G. (2017). Improved dynamic dependability assessment through integration with prognostics. IEEE Transactions on Reliability, 66(3), 893-913.
[8] Aizpurua, J. I., McArthur, S. D., Stewart, B. G., Lambert, B., Cross, J. G., & Catterson, V. M. (2018). Adaptive power transformer lifetime predictions through machine learning and uncertainty modeling in nuclear power plants. IEEE Transactions on Industrial Electronics, 66(6), 4726-4737.


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